The following relates to image processing, image retrieval, image archiving, and related arts. It finds particular application in a system and method for segmenting an image into two or more regions, based on multiple sources of information, such as visible and infrared (IR) information.
Automated tagging or classification of images is useful for diverse applications such as image archiving, image retrieval, and so forth. In a typical approach, the image is represented by a high level image signature that is often based on cumulating local information. These image signatures are then used as inputs to a trained classifier that outputs a class label (or vector of class label probabilities, in the case of a soft classifier) for the image.
For many applications involving images, such as photographic images or visual documents, it is desirable to go beyond tagging and to perform image segmentation. This can be used for better understanding of a scene or for locating and segmenting a particular object (e.g., the license plate of a car, a particular soft-drink can, identifying and counting different products on shelves in a store, etc.). The segmentation may involve segmenting the entire image according to class labels, such as sky, road, people, buildings, tissue types, etc. For example, pixel-level masks may be created where each pixel is assigned a label.
Object segmentation tends to be a difficult task, since images often include background information which can be confused with an object of interest. A variety of approaches have been proposed. Many of these rely on detecting contours in the image, in order to estimate the most likely object border for an object of interest. For example, graph based methods have been used, which incorporate a recognition part (in which object is this pixel?) and a regularization part, in which neighboring image pixels are urged to belong to the same region (e.g., object or background). The regularization part can also include some of the contour information.
Such segmentation algorithms are still prone to errors in the presence of a cluttered background, or distracting objects. Even when the objects themselves can be successfully recognized, the contour of the objects can be confused with strong edges coming from the background clutter, or any distracting objects.
The exemplary embodiment provides a system and method for object segmentation which employs different sources of information to improve the segmentation process.